Identification and immunological characterization of cuproptosis-related molecular clusters in ischemic stroke.

ABSTRACT: The present study elucidated cuproptosis-related molecular clusters involved in ischemic stroke and developed predictive models. Transcriptomic and immunological profiles of ischemic stroke-related datasets were extracted from the Gene Expression Omnibus database. Next, we conducted weighted gene co-expression network analysis to determine cluster-specific differentially expressed genes (DEGs). Models such as random forest and eXtreme gradient boosting (XGB) were evaluated to select the best prediction performance model. Subsequently, we validated the model's predictive efficiency by using nomograms, decision curve analysis, calibration curves, and receiver operating characteristic curve analysis with an external dataset. We identified two cuproptosis-related clusters involved in ischemic

SUBMITTER: Liu C 

PROVIDER: S-EPMC10702694 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature